SNeRG The SNePS Research Group
Prof. Stuart C. Shapiro, Prof. William J. Rapaport
Prof. Carl Alphonce, Prof. Josephine Anstey, Prof. Debra T. Burhans, Prof. Michelle L. Gregory, Prof. Jean-Pierre A. Koenig, Prof. David R. Pierce
Graduate Students: Jonathan Bona, Trupti Devdas Nayak, Albert Goldfain, Frances L. Johnson, Michael Kandefer, John F. Santore, Lunarso Sutanto
Undergraduate Students: Vikranth B. Rao, Isidore DingaMadou
Semantic Network Processing System
The long-term goal of The SNePS Research Group is the design and construction of a natural-language-using computerized cognitive agent, and carrying out the research in artificial intelligence,
computational linguistics, and cognitive science necessary for that endeavor. The three-part focus of the group is on knowledge representation, reasoning, and natural-language understanding
and generation. The group is widely known for its development of the SNePS knowledge representation/reasoning system, and Cassie, its computerized cognitive agent.
Cassie the FEVAHR Agent
GLAIR Architecture
Grounded Layered Architecture with Integrated Reasoning A Plan for Detonating Unexploded
Landmines (UXOs)
Knowledge Level all(a)(Agent(a) =>
NL ActPlan(Blowup(a, UXOs),
SNePS Act(a,
Cascade(SearchforUxo(a),
Perceptuo-Motor Level WithSome+(obj,
Near(a, obj),
WithNew({ch ex},
{Charge(ch), Explosion(ex)},
Possess(a, ch),
Sensory-Actuator Level Vision Cascade(Place(a, ch, obj),
Natural Language Interaction with Hide(a),
FEVAHR (Foveal Extra-Vehicular Sonar Waitfor(a, ex),
Activity Helper-Retriever) Cassie SearchforUxo(a))),
Motion
: Who are you? goto(a, SafeZone))))))
My name is `Cassie'
and I am the SNePS cognitive agent. Proprioception
: Who did you talk to? Example SNePS Ontology
I talked to Stu
and I talked to Bill
and I talked to Carl
and I talked to David
and I talked to Debbie
and I talked to J.P.
and I talked to Josephine
and I talked to Michelle
and I am talking to you.
: Who did you see? The Trial The Trail is an
I saw Stu interactive drama for an immersive
and I saw Albert VR environment. Its intelligent agents
are SNePS-driven.
and I saw Fran
and I saw John
and I saw Jon
and I saw Lunarso
and I saw Michael
and I saw Trupti
Contextual Vocabulary Acquisition:From Algorithm to Curriculum
and I see you.
Belief Base Revision with Reconsideration PIs: William J. Rapaport (CSE & SNeRG) & Michael W. Kibby (Learning & Instruction Dept.)
• CVA = computing a meaning for unknown word from contextual clues & prior knowledge
UAV Current Belief Base •“There came a white hart running into the hall with a white brachet next to him, and thirty couples of black hounds came running
after them. As the hart went by the sideboard, the white brachet bit him. The knight arose, took up the brachet and rode away
Time UAV INTEL TROOPS
with the brachet. A lady came in and cried aloud to King Arthur, ‘Sire, the brachet is mine’. There was the white brachet which
T1 Red in D1,D2 Red in D1,D3 INTEL bayed at him fast. The hart lay dead; a brachet was biting on his throat, and other hounds came behind.” [Morte D’Arthur]
T2 Red in D1,D2 Red in D1,D3,C3,Bridge-D •Cassie learns what “brachet” means: From above text + prior knowledge about harts, animals, King Arthur, etc.; no info
about brachetsInput: SNePS version of simplified English narrative. Output: Definition frame (varies with context and prior
T3 Red in D1,D2 Red in D1,D3,C3,D4 Red in C3 knowledge):
1.First Sentence:
Always TROOPS > UAV > INTEL • A hart runs into King Arthur’s hall. 3. Full Story:
– In the story, B12 is a hart. A hart runs into King Arthur’s hall.
Always TROOPS > INTEL > UAV
– In the story, B13 is a hall. A white brachet is next to the hart.
– In the story, B13 is King Arthur’s. The brachet bites the hart’s buttock.
Asserting beliefs into the belief base (or KB) = Adding them to the KB = Stating them to
be true. T1: UAV & INTEL disagree on Red troop location => contradiction. – In the story, B12 runs into B13 The knight picks up the brachet.
Consolidation makes a belief base consistent -- in this case by removing (or retracting) • A white brachet is next to the hart. The knight carries the brachet.
INTEL’s statement. = Contracting the KB by INTEL’s statement. (UAV > INTEL) BLUE – In the story, B14 is a brachet. The lady says that she wants the brachet.
T2: UAV & INTEL again disagree. TROOPS – In the story, B14 has the property “white”. The brachet bays at Sir Tor.
At T3, BLUE TROOPS confirm an INTEL belief over that of UAV Therefore, brachets are physical objects. • + prior knowledge: only hunting dogs bay
So, we reverse the INTEL/UAV credibility order. Thus, UAV is disbelieved. • deduced while reading, using… 4.--> (defineNoun “brachet”)
Reconsideration of the KB is defined as consolidation of all base beliefs (current, or not). •…prior knowledge: only physical objects have color Definition of brachet:
INTEL’s earlier beliefs are recaptured (= returned to the KB), and UAV’s are retracted.
2.--> (defineNoun “brachet”) Class Inclusions: hound, dog,
Definition of brachet: Possible Actions: bite buttock, bay, hunt,
Class Inclusions: phys obj, Possible Properties: valuable, small, white,
Possible Properties: white, 5. OED: brachet: a kind of hound which hunts by scent
• Application: Development of classroom curriculum to teach CVA, based on our CVA algorithms
Identifying Perceptually Indistinguishable Objects:
Is that the same one you saw before?
Crystal Cassie’s view of the world showing two perceptually indistinguishable robots, Robots used in human subjects’ and Crystal Cassie’s What Crystal Cassie can see: a table with glasses and a computer lab with two people
one of whom she is following. tasks
SNeRG website: www.cse.buffalo.edu/sneps